Computational Engineering of Allulose-Responsive Biosensors for Auto-Inducible Protein Expression and Dynamic CRISPRi Metabolic Control

Transcription-factor-based biosensors are powerful tools for programmable metabolic engineering, offering precise control over gene expression in response to specific metabolites. In this study, the authors present a computational and structure-guided approach to redesign the allulose-responsive biosensor PsiR, significantly improving its sensitivity and dynamic range.

By integrating protein structure analysis and computational modeling, the research team iteratively optimized PsiR, enabling robust allulose-triggered gene expression. This enhanced biosensor toolbox was further leveraged for auto-inducible protein production and dynamic metabolic regulation using CRISPR interference (CRISPRi) systems.

The improved PsiR biosensor demonstrated superior performance in sensing allulose, a rare sugar with growing applications in food and biotechnology. The platform supports programmable, metabolite-driven gene expression, facilitating advanced metabolic engineering strategies such as dynamic pathway balancing and feedback regulation.

This work highlights the synergy between computational protein design and synthetic biology, paving the way for next-generation biosensors tailored for precise metabolic control and industrial biotechnology applications.

• Weidemüller, P., Kholmatov, M., Petsalaki, E. & Zaugg, J. B. Transcription factors: bridge between cell signaling and gene regulation. Proteomics 21, e2000034 (2021).